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This document describes results from the OHI 2016 global assessment.

Scores are broken down as follows:

  1. Description of reference datasets and figures
  2. The global picture
  3. A closer look at regions
  4. Change over time: five year trends in scores
  5. Comparison of assessment years (what’s changed do due to changes to models and/or data sources)
  6. A closer look at goals
  7. Additional checks

Description of reference datasets and figures

The following datasets and figures include the complete data for the OHI 2016 assessment.

Datasets (compiled for website)

Data files to generate content for the Ocean Health Index website.

Output files:

  • radical_date.csv. This file is also used to generate the tables and figures in this document.
    • File includes the EEZ (updated in 2012-2016), Antarctica (most recently calculated in 2015), and High Seas (most recently calculated in 2014) data. However, years 2012 and 2013 do not have Antarctica or High Seas data. And, the same High Seas and Antarctica data are used for years 2014, 2015, and 2016.
    • Region 0 is the global average scores, which are calculated using an area-weighted average of the scores (status, likely future state, and score dimensions) for EEZ, Antarctica, and High Seas regions.
    • Region 300 is the eez average scores, which are calculated using an area-weighted average of the scores (status, likely future state, and score dimensions) for the EEZ regions.
  • DataExplorer_eez2016_date.csv. This file includes only the 2016 data, and is used in the Data Explorer.

Figures that provide an overview of scores

This carpet plot figure (download as high resolution png) provides a full overview of the scores from the 2016 assessment. Each row represents a region, the main groupings represent goals, and within each goal there are 5 years of data. Black regions indicate no data.

Don’t try too hard to interpret the results for specific countries/goals/years!!

This plot is good for providing a quick overview of things like:

  • What is the range of scores?
  • Which goals tend to have high scores across most regions (species, habitat)
  • Which goals have a lot of variation across regions (tourism & recreation, lasting special places)
  • Which goals are volatile across years (natural products, tourism & recreation)

Another resource that can be useful for examining scores is this interactive plot. This can be used to (some example screen shots):

explore the distribution of scores

compare different goal scores

observe change over time

More figure resources

  • The location of maps describing goal and index scores can be downloaded from here
  • Flower plots for each region can be downloaded from here

The global picture

This section describes global patterns in index and goal scores. If you are more interested in what is happening at the region scale, skip to the next section.

Average global goal and index scores across years

(NOTE: Livelihood and economy goals are not included here)

Overall, there weren’t dramatic changes across years.

The Index score for 2016 (eez area weighted average of region scores) was: 71. This value was essentially constant from 2012 to 2016.

I haven’t done a formal analysis, but it looks like over time:

  • Clean water scores may be slightly decreasing
  • Lasting special places may be slightly increasing
  • Natural products decreases rather dramatically
goal 2012 2013 2014 2015 2016
Index 71.1 71.5 71.0 71.0 70.6
Artisanal opportunities 76.6 76.9 77.2 77.2 77.2
Species condition (subgoal) 91.1 91.1 91.1 91.2 91.2
Biodiversity 90.8 90.8 90.8 90.9 90.9
Habitat (subgoal) 90.4 90.5 90.5 90.5 90.6
Coastal protection 87.8 87.7 87.7 87.6 87.3
Carbon storage 79.2 79.2 79.2 79.2 79.3
Clean water 74.2 73.7 73.6 73.5 73.5
Fisheries (subgoal) 53.2 53.5 53.5 52.9 53.4
Food provisioning 53.3 53.6 53.7 52.9 53.2
Mariculture (subgoal) 30.1 32.8 33.7 32.8 31.9
Iconic species (subgoal) 66.2 67.4 67.4 67.8 66.5
Sense of place 61.1 62.6 62.6 63.1 62.4
Lasting special places (subgoal) 56.1 57.7 57.8 58.5 58.4
Natural products 59.0 58.0 56.5 53.2 48.0
Tourism & recreation 49.9 48.7 45.3 48.1 49.2

A closer look at the regions

This section explores goal and index scores at the region level. I mostly focus on the 2016 scores.

Regional goal and index scores for 2016

This interactive table describes the index and goal scores for the regions in 2016 (and here’s a link to a color coded table, and a csv file can also be downloaded).

Distribution of scores

The median index score was 68. The highest score was 91 for Howland Island and Baker Island, and the lowest score was 44 for Sierra Leone.

The following histogram describes the distribution of overall index scores:

The regions with index scores of 80 or greater are:

country Index
Howland Island and Baker Island 91
Jarvis Island 89
South Georgia and the South Sandwich Islands 88
Christmas Island 85
Seychelles 85
Palmyra Atoll 85
Germany 85
Northern Saint-Martin 84
Cocos Islands 83
Phoenix Islands (Kiribati) 83
New Caledonia 82
Crozet Islands 82
Kerguelen Islands 82
Heard and McDonald Islands 82
Norfolk Island 81
Macquarie Island 81
Antigua and Barbuda 81
American Samoa 81
Aruba 81
Australia 80
Glorioso Islands 80

The regions with index scores of 50 or less are:

country Index
Eritrea 50
Senegal 50
Lebanon 50
Algeria 50
Republique du Congo 49
Guinea Bissau 49
Liberia 48
Nicaragua 47
Democratic Republic of the Congo 47
Guinea 45
Ivory Coast 45
Libya 44
Sierra Leone 44

Map of index scores

Map png files can be downloaded here.

(Maps of goal scores are described below)

Comparing assessment years

The following is a comparison of the global status scores generated for 2015 by this year’s assessment vs. last year’s assessment.

If the models and source data remains the same, these scores should be exactly the same. Differences indicate changes in methods or source data (described in this downloadable document).

These changes do not reflect changes in actual system health!

Global averages

The following goals had the largest changes:

  • Artisanal opportunity (biggest change: +11 points): Reference point of need data adjusted to avoid scores being driven by outliers
  • Species condition/biodiversity (+8 points): Addition of bird species tended to increase status scores
  • Mariculture (+6 points): Changes to source data
  • Fisheries (-4 points): Decrease is due primarily to increase in number of stocks classified as “unidentified” in source data (plus, a lot of other changes)

The rest of the goals/subgoals changed by less than 3 points (on average, although regions might be highly variable).

goal assess2015 assess2016 change
Artisanal opportunities 62.92 73.53 10.61
Species condition (subgoal) 85.18 92.78 7.60
Biodiversity 86.52 90.35 3.83
Habitat (subgoal) 87.90 87.91 0.01
Coastal protection 85.64 85.53 -0.11
Carbon storage 78.42 78.42 0.00
Clean water 73.47 73.47 0.00
Economies 87.65 87.65 0.00
Livelihoods & economies 82.46 82.46 0.00
Livelihoods 77.28 77.28 0.00
Fisheries (subgoal) 54.91 50.48 -4.43
Food provisioning 54.25 50.50 -3.75
Mariculture (subgoal) 25.44 31.53 6.09
Iconic species (subgoal) 59.27 63.11 3.84
Sense of place 58.66 59.76 1.10
Lasting special places (subgoal) 58.54 56.41 -2.13
Natural products 48.71 50.91 2.20
Tourism & recreation 48.29 46.98 -1.31

Regional data

This color-coded table compares the 2015 index scores generated for each region/goal between this year’s and last year’s assessment.

Because these are index scores, changes reflect updates to pressure and resilience scores as well as status.

The following interactive plot provides an overview of how the 2015 scores changed between the 2015 and 2016 assessment for all goals and dimensions.

Some general pattern in these data:

  • Fisheries/Food provisioning: Lots of changes all over the place due to changes in source data and other changes to methods
  • Artisanal opportunity: consistently increased for most regions due to change in reference point

A closer look at goals

This section takes a closer look at each goal/subgoal. I do not include goals that are comprised of multiple subgoals, although the subgoals are described.

Artisanal opportunities

Scores

Top 10 performers

country score
Cayman Islands 100
United Arab Emirates 100
Qatar 100
Bermuda 100
United States 100
Kuwait 100
Norway 100
Ireland 100
Saudi Arabia 100
Brunei 100

Bottom 10 performers

country score
211 Ivory Coast 46
212 Solomon Islands 45
213 Cameroon 45
214 Mozambique 44
215 Comoro Islands 44
216 Madagascar 44
217 Benin 44
218 Togo 43
219 Guinea 43
220 Liberia 42

Species condition

Scores

Top 10 performers

country score
Northern Saint-Martin 98
Aruba 98
Curacao 98
Cayman Islands 98
Ascension 98
Bahamas 98
Saba 98
Montserrat 98
Bonaire 98
Turks and Caicos Islands 98

Bottom 10 performers

country score
211 Vietnam 82
212 Singapore 82
213 Eritrea 82
214 Oecussi Ambeno 81
215 Cambodia 81
216 East Timor 81
217 Libya 81
218 Myanmar 80
219 Sudan 80
220 Iraq 78

Habitat

Scores

Top 10 performers

country score
Kerguelen Islands 100
Heard and McDonald Islands 100
Norfolk Island 100
Macquarie Island 100
Tuvalu 100
Pitcairn 100
Wallis and Futuna 100
British Indian Ocean Territory 100
Suriname 100
Russia 100

Bottom 10 performers

country score
208 Colombia 66
209 Nigeria 66
210 Democratic Republic of the Congo 64
211 Belize 62
212 Jan Mayen 61
213 Senegal 61
214 Poland 60
215 Dominica 60
216 Sierra Leone 59
217 Iceland 52

Coastal protection

Scores

Top 10 performers

country score
Howland Island and Baker Island 100
Phoenix Islands (Kiribati) 100
Aruba 100
Curacao 100
Tuvalu 100
Pitcairn 100
French Polynesia 100
Wallis and Futuna 100
Netherlands 100
British Indian Ocean Territory 100

Bottom 10 performers

country score
161 Ivory Coast 33
162 Guinea 31
163 Sierra Leone 31
164 Senegal 30
165 Guinea Bissau 30
166 Nicaragua 30
167 Democratic Republic of the Congo 30
168 Lithuania 29
169 Dominica 27
170 Belize 24

Carbon storage

Scores

Top 10 performers

country score
Seychelles 100
Germany 100
Northern Saint-Martin 100
Antigua and Barbuda 100
Aruba 100
Netherlands 100
Bahamas 100
Suriname 100
Russia 100
South Africa 100

Bottom 10 performers

country score
139 Senegal 34
140 Liberia 34
141 Guinea 33
142 Ivory Coast 33
143 Sierra Leone 33
144 Guinea Bissau 31
145 Democratic Republic of the Congo 30
146 Barbados 27
147 Dominica 27
148 Nicaragua 10

Clean waters

Scores

Top 10 performers

country score
Heard and McDonald Islands 100
South Georgia and the South Sandwich Islands 99
Kerguelen Islands 99
Falkland Islands 99
Bouvet Island 99
Jarvis Island 98
Macquarie Island 98
Howland Island and Baker Island 97
Phoenix Islands (Kiribati) 97
Crozet Islands 97

Bottom 10 performers

country score
211 Israel 33
212 Guatemala 33
213 Belgium 32
214 India 29
215 Benin 29
216 Lebanon 29
217 Slovenia 28
218 Togo 28
219 Monaco 24
220 Gibraltar 20

Fisheries

Scores

Top 10 performers

country score
Tuvalu 96
Nauru 96
Palau 95
Mayotte 91
Oecussi Ambeno 91
Phoenix Islands (Kiribati) 90
Finland 90
Maldives 89
Seychelles 88
Solomon Islands 88

Bottom 10 performers

country score
211 El Salvador 19
212 Amsterdam Island and Saint Paul Island 18
213 Mauritania 17
214 Barbados 17
215 Western Sahara 16
216 Guinea 16
217 Wake Island 12
218 Turks and Caicos Islands 9
219 Jan Mayen 6
220 Bouvet Island 3

Mariculture

Scores

Top 10 performers

country score
Russia 100
Ecuador 100
New Zealand 100
Norway 100
Chile 100
China 100
Faeroe Islands 100
Iceland 99
Belize 94
Canada 72

Bottom 10 performers

country score
116 Kenya 0
117 Jamaica 0
118 Nigeria 0
119 El Salvador 0
120 Pakistan 0
121 Eritrea 0
122 Senegal 0
123 Lebanon 0
124 Algeria 0
125 Libya 0

Iconic species

Scores

Top 10 performers

country score
Cayman Islands 100
United Arab Emirates 100
Qatar 100
Bermuda 100
United States 100
Kuwait 100
Norway 100
Ireland 100
Saudi Arabia 100
Brunei 100

Bottom 10 performers

country score
211 Ivory Coast 46
212 Solomon Islands 45
213 Cameroon 45
214 Mozambique 44
215 Comoro Islands 44
216 Madagascar 44
217 Benin 44
218 Togo 43
219 Guinea 43
220 Liberia 42

Lasting special places

Scores

Top 10 performers

country score
Howland Island and Baker Island 100
Jarvis Island 100
South Georgia and the South Sandwich Islands 100
Palmyra Atoll 100
Germany 100
Northern Saint-Martin 100
Phoenix Islands (Kiribati) 100
Crozet Islands 100
Kerguelen Islands 100
Heard and McDonald Islands 100

Bottom 10 performers

country score
211 Bouvet Island 0
212 Benin 0
213 Iraq 0
214 Syria 0
215 Sudan 0
216 Somalia 0
217 North Korea 0
218 Eritrea 0
219 Liberia 0
220 Libya 0

Natural products

Scores

Top 10 performers

country score
New Caledonia 100
Suriname 100
Mozambique 100
India 99
Iran 98
French Polynesia 97
Trinidad and Tobago 97
Germany 96
Italy 96
Latvia 96

Bottom 10 performers

country score
119 Honduras 0
120 Brunei 0
121 Faeroe Islands 0
122 Cyprus 0
123 Sao Tome and Principe 0
124 Montenegro 0
125 Equatorial Guinea 0
126 Dominica 0
127 Algeria 0
128 Republique du Congo 0

Tourism and recreation

Scores

Top 10 performers

country score
Seychelles 100
Northern Saint-Martin 100
Antigua and Barbuda 100
Aruba 100
Malta 100
Curacao 100
Vanuatu 100
Maldives 100
Bahamas 100
Saba 100

Bottom 10 performers

country score
195 Democratic Republic of the Congo 2
196 Yemen 0
197 Ukraine 0
198 Iraq 0
199 Turkey 0
200 Syria 0
201 Somalia 0
202 North Korea 0
203 Lebanon 0
204 Libya 0

Additional checks

This table describes the years of data that were used for each goal for each assessment year.

Years used in analysis
goals eez_2012 eez_2013 eez_2014 eez_2015 eez_2016
AO 2011 2012 2013 2014 2015
SPP NA NA NA NA NA
BD NA NA NA NA NA
HAB NA NA NA NA NA
CP NA NA NA NA NA
CS NA NA NA NA NA
CW NA NA NA NA NA
ECO 20122000 20122000 20122000 20122000 20122000
LE NA NA NA NA NA
LIV 20102000 20112000 20122000 20122000 20122000
FIS 2006 2007 2008 2009 2010
FP NA NA NA NA NA
MAR 2010 2011 2012 2013 2014
ICO 2012 2013 2014 2015 2016
SP NA NA NA NA NA
LSP 2011 2012 2013 2014 2015
NP 2009 2010 2011 2012 2013
TR 2010 2011 2012 2013 2014

Analyses of data

One thing we have been interested in is how well the likely future state score actually predicts the future score.

My initial conclusions are that the trend/pressure/resilience components of the likely future state score are not improving our predictions.

Observed vs. predicted 2016 scores

Methods: I compared the likely future status scores in 2012 to the observed status in 2016.

Results: At first glance, this appears promising because there is actaully a nice correlation between the values:

And, the slope estimate isn’t too far from 1 (0.76) and the R2 value is fairly high (0.75):

## 
## Call:
## lm(formula = status_2016 ~ likely_future_state_2012, data = data_sp)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -13.6357  -2.8018   0.1621   2.8615  11.0528 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              21.00566    2.01076   10.45   <2e-16 ***
## likely_future_state_2012  0.65202    0.02876   22.67   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.509 on 218 degrees of freedom
## Multiple R-squared:  0.7022, Adjusted R-squared:  0.7009 
## F-statistic: 514.1 on 1 and 218 DF,  p-value: < 2.2e-16

Scores 2012 vs. scores 2016

Methods: I compared the 2012 and 2016 status scores to get a feel for how well the 2012 scores predicted the 2016 scores.

Results: The 2012 scores alone do a better job predicting 2016 scores than incorporating the trend/pressure/resilience data. The additional information seems to, overall, make our predictions worse:

## 
## Call:
## lm(formula = status_2016 ~ status_2012, data = data_sp)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -17.9300  -1.4938   0.2677   1.7784  12.1589 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  5.01133    2.09737   2.389   0.0177 *  
## status_2012  0.92162    0.03143  29.323   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.716 on 218 degrees of freedom
## Multiple R-squared:  0.7977, Adjusted R-squared:  0.7968 
## F-statistic: 859.8 on 1 and 218 DF,  p-value: < 2.2e-16

Predicted change in score vs. observed change in score (from 2012 to 2016)

Methods: Another way to look at these ata is to compare the predicted and observed changes in status from 2012 to 2016.

The predicted change in status was calculated as: status (2012) minus likely future score (2012). The observed change in score was calcualted as status (2012) minus status (2016).

Results: There was no correlation between the predicted change in status and the observed change in status:

## 
## Call:
## lm(formula = obs_change ~ pred_change, data = data_sp)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -17.9672  -1.5766   0.2428   2.0001  11.2743 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.33757    0.29703  -1.136    0.257
## pred_change  0.05436    0.05405   1.006    0.316
## 
## Residual standard error: 3.76 on 218 degrees of freedom
## Multiple R-squared:  0.004617,   Adjusted R-squared:  5.151e-05 
## F-statistic: 1.011 on 1 and 218 DF,  p-value: 0.3157

Looking more closely at patterns within goals/subgoals

The next step in the above analysis is to look within each goal/subgoal to get a better feel for possible relationships between trend and pressure/resilience components.

AO: A closer look

## 
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.4336  -0.4367  -0.1202   0.5684   4.0700 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  0.43477    0.16011   2.715  0.00715 **
## pred_change  0.01732    0.01844   0.940  0.34850   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.308 on 218 degrees of freedom
## Multiple R-squared:  0.004033,   Adjusted R-squared:  -0.0005358 
## F-statistic: 0.8827 on 1 and 218 DF,  p-value: 0.3485
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.6640 -0.4712 -0.1182  0.5061  4.6456 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) 0.330908   0.183614   1.802  0.07290 . 
## trend_2012  8.510248   3.056197   2.785  0.00583 **
## r_minus_p   0.006277   0.004306   1.458  0.14634   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.288 on 217 degrees of freedom
## Multiple R-squared:  0.03917,    Adjusted R-squared:  0.03031 
## F-statistic: 4.423 on 2 and 217 DF,  p-value: 0.0131
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012, 
##     data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.6554 -0.4050 -0.0943  0.4460  4.7771 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     -1.73704    1.10443  -1.573  0.11723   
## trend_2012       8.11897    3.04500   2.666  0.00825 **
## pressures_2012   0.01166    0.01037   1.124  0.26218   
## resilience_2012  0.02780    0.01212   2.294  0.02274 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.28 on 216 degrees of freedom
## Multiple R-squared:  0.05494,    Adjusted R-squared:  0.04181 
## F-statistic: 4.186 on 3 and 216 DF,  p-value: 0.006625

BD/SPP: A closer look

## 
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0      NA       NA
## pred_change        0          0      NA       NA
## 
## Residual standard error: 0 on 218 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 1 and 218 DF,  p-value: NA
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0      NA       NA
## trend_2012         0          0      NA       NA
## r_minus_p          0          0      NA       NA
## 
## Residual standard error: 0 on 217 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 2 and 217 DF,  p-value: NA
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012, 
##     data = data_g)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)
## (Intercept)            0          0      NA       NA
## trend_2012             0          0      NA       NA
## pressures_2012         0          0      NA       NA
## resilience_2012        0          0      NA       NA
## 
## Residual standard error: 0 on 216 degrees of freedom
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 3 and 216 DF,  p-value: NA

BD/HAB: A closer look

## 
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -18.0323  -0.1645   0.1110   0.3672  15.0375 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -0.37627    0.18689  -2.013  0.04533 * 
## pred_change  0.06486    0.02396   2.708  0.00732 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.148 on 215 degrees of freedom
##   (3 observations deleted due to missingness)
## Multiple R-squared:  0.03297,    Adjusted R-squared:  0.02848 
## F-statistic: 7.331 on 1 and 215 DF,  p-value: 0.007323
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -17.8677  -0.0233   0.0844   0.1844  15.7936 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.206043   0.297167  -0.693    0.489
## trend_2012   1.496213   1.148257   1.303    0.194
## r_minus_p    0.004269   0.007678   0.556    0.579
## 
## Residual standard error: 2.188 on 212 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.009962,   Adjusted R-squared:  0.0006217 
## F-statistic: 1.067 on 2 and 212 DF,  p-value: 0.346
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012, 
##     data = data_g)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -17.5544  -0.1430   0.0663   0.2980  16.0468 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)
## (Intercept)      2.45789    2.00787   1.224    0.222
## trend_2012       1.73076    1.15936   1.493    0.137
## pressures_2012  -0.02623    0.01808  -1.451    0.148
## resilience_2012 -0.02202    0.02104  -1.046    0.297
## 
## Residual standard error: 2.184 on 211 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.01833,    Adjusted R-squared:  0.004377 
## F-statistic: 1.314 on 3 and 211 DF,  p-value: 0.2709

CP: A closer look

## 
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -43.506   0.665   0.872   1.239  20.996 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.66474    0.54834  -1.212    0.227
## pred_change -0.07333    0.05887  -1.246    0.215
## 
## Residual standard error: 6.556 on 168 degrees of freedom
##   (50 observations deleted due to missingness)
## Multiple R-squared:  0.00915,    Adjusted R-squared:  0.003252 
## F-statistic: 1.551 on 1 and 168 DF,  p-value: 0.2147
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -36.932   0.376   0.713   1.218  24.687 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.508754   0.935732  -0.544    0.587    
## trend_2012  -9.485762   2.254036  -4.208 4.41e-05 ***
## r_minus_p   -0.009563   0.026235  -0.364    0.716    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.575 on 150 degrees of freedom
##   (67 observations deleted due to missingness)
## Multiple R-squared:  0.108,  Adjusted R-squared:  0.09615 
## F-statistic: 9.085 on 2 and 150 DF,  p-value: 0.0001887
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012, 
##     data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -35.560  -0.485   0.634   1.734  26.384 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     10.14953    5.11064   1.986  0.04887 * 
## trend_2012      -7.83222    2.36072  -3.318  0.00114 **
## pressures_2012  -0.13325    0.07217  -1.846  0.06682 . 
## resilience_2012 -0.09371    0.04740  -1.977  0.04991 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.5 on 149 degrees of freedom
##   (67 observations deleted due to missingness)
## Multiple R-squared:  0.1342, Adjusted R-squared:  0.1167 
## F-statistic: 7.697 on 3 and 149 DF,  p-value: 8.139e-05

CS: A closer look

## 
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0      NA       NA
## pred_change        0          0      NA       NA
## 
## Residual standard error: 0 on 146 degrees of freedom
##   (72 observations deleted due to missingness)
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 1 and 146 DF,  p-value: NA
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0          0      NA       NA
## trend_2012         0          0      NA       NA
## r_minus_p          0          0      NA       NA
## 
## Residual standard error: 0 on 122 degrees of freedom
##   (95 observations deleted due to missingness)
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 2 and 122 DF,  p-value: NA
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012, 
##     data = data_g)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
##      0      0      0      0      0 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)
## (Intercept)            0          0      NA       NA
## trend_2012             0          0      NA       NA
## pressures_2012         0          0      NA       NA
## resilience_2012        0          0      NA       NA
## 
## Residual standard error: 0 on 121 degrees of freedom
##   (95 observations deleted due to missingness)
## Multiple R-squared:    NaN,  Adjusted R-squared:    NaN 
## F-statistic:   NaN on 3 and 121 DF,  p-value: NA

CW: A closer look

## 
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.4848 -0.3783 -0.0283  0.1962  7.3090 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.030160   0.079765   0.378    0.706
## pred_change -0.010548   0.008351  -1.263    0.208
## 
## Residual standard error: 1.178 on 218 degrees of freedom
## Multiple R-squared:  0.007264,   Adjusted R-squared:  0.002711 
## F-statistic: 1.595 on 1 and 218 DF,  p-value: 0.2079
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.5176 -0.3544 -0.0330  0.2651  7.0581 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.101058   0.093086   1.086   0.2788  
## trend_2012   0.153175   0.528110   0.290   0.7721  
## r_minus_p   -0.005819   0.003129  -1.860   0.0643 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.175 on 217 degrees of freedom
## Multiple R-squared:  0.01602,    Adjusted R-squared:  0.006956 
## F-statistic: 1.767 on 2 and 217 DF,  p-value: 0.1733
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012, 
##     data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.4460 -0.3816 -0.0369  0.2376  7.0313 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      0.662778   0.596572   1.111    0.268  
## trend_2012       0.117102   0.529575   0.221    0.825  
## pressures_2012   0.001485   0.005519   0.269    0.788  
## resilience_2012 -0.009948   0.005344  -1.861    0.064 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.176 on 216 degrees of freedom
## Multiple R-squared:  0.02015,    Adjusted R-squared:  0.006538 
## F-statistic:  1.48 on 3 and 216 DF,  p-value: 0.2208

FP/FIS: A closer look

## 
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -37.285  -3.099  -0.290   1.872  39.356 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   1.0398     0.6562   1.585    0.115
## pred_change   0.1397     0.1080   1.294    0.197
## 
## Residual standard error: 7.849 on 218 degrees of freedom
## Multiple R-squared:  0.007617,   Adjusted R-squared:  0.003065 
## F-statistic: 1.673 on 1 and 218 DF,  p-value: 0.1972
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.113  -2.229  -0.591   1.097  38.550 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.41789    0.83929   2.881 0.004363 ** 
## trend_2012  13.98954    3.97192   3.522 0.000522 ***
## r_minus_p   -0.02157    0.02351  -0.917 0.360094    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.655 on 217 degrees of freedom
## Multiple R-squared:  0.06054,    Adjusted R-squared:  0.05188 
## F-statistic: 6.991 on 2 and 217 DF,  p-value: 0.001142
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012, 
##     data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.311  -2.317  -0.569   1.125  38.293 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     -0.212763   7.281350  -0.029 0.976716    
## trend_2012      14.329206   4.087981   3.505 0.000555 ***
## pressures_2012   0.039772   0.055324   0.719 0.472979    
## resilience_2012  0.007554   0.083457   0.091 0.927965    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.67 on 216 degrees of freedom
## Multiple R-squared:  0.06111,    Adjusted R-squared:  0.04807 
## F-statistic: 4.686 on 3 and 216 DF,  p-value: 0.003419

FP/MAR: A closer look

## 
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -35.625  -0.245   1.026   1.043  40.021 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -1.0359     0.6847  -1.513    0.133    
## pred_change   0.7460     0.1517   4.917 2.75e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.075 on 123 degrees of freedom
##   (95 observations deleted due to missingness)
## Multiple R-squared:  0.1643, Adjusted R-squared:  0.1575 
## F-statistic: 24.18 on 1 and 123 DF,  p-value: 2.75e-06
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.283  -1.703   0.001   1.631  59.025 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  1.00176    1.56065   0.642   0.5222  
## trend_2012   2.20126    1.25171   1.759   0.0812 .
## r_minus_p   -0.01993    0.03889  -0.512   0.6093  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.687 on 121 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.02952,    Adjusted R-squared:  0.01348 
## F-statistic:  1.84 on 2 and 121 DF,  p-value: 0.1632
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012, 
##     data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -32.716  -2.063  -0.111   1.755  58.101 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      8.72983    5.24639   1.664   0.0987 .
## trend_2012       2.54574    1.26453   2.013   0.0463 *
## pressures_2012  -0.07680    0.07369  -1.042   0.2994  
## resilience_2012 -0.08913    0.05924  -1.505   0.1351  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.644 on 120 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.04838,    Adjusted R-squared:  0.02459 
## F-statistic: 2.034 on 3 and 120 DF,  p-value: 0.1128

SP/ICO: A closer look

## 
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.5406  -0.5394  -0.2818   0.5419   4.4507 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.574520   0.231172   2.485   0.0137 *
## pred_change -0.007735   0.026319  -0.294   0.7691  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.636 on 218 degrees of freedom
## Multiple R-squared:  0.000396,   Adjusted R-squared:  -0.004189 
## F-statistic: 0.08637 on 1 and 218 DF,  p-value: 0.7691
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.4913  -0.5583  -0.3083   0.5431   4.5245 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) 0.460498   0.236643   1.946    0.053 .
## trend_2012  0.327503   3.005825   0.109    0.913  
## r_minus_p   0.001393   0.006128   0.227    0.820  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.64 on 217 degrees of freedom
## Multiple R-squared:  0.0003137,  Adjusted R-squared:  -0.0089 
## F-statistic: 0.03405 on 2 and 217 DF,  p-value: 0.9665
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012, 
##     data = data_g)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.5547  -0.5951  -0.2968   0.5700   4.5240 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)     -0.625499   1.363389  -0.459    0.647
## trend_2012       0.279833   3.008800   0.093    0.926
## pressures_2012   0.006961   0.012012   0.580    0.563
## resilience_2012  0.012584   0.015135   0.831    0.407
## 
## Residual standard error: 1.642 on 216 degrees of freedom
## Multiple R-squared:  0.003332,   Adjusted R-squared:  -0.01051 
## F-statistic: 0.2407 on 3 and 216 DF,  p-value: 0.8679

SP/LSP: A closer look

## 
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.684 -3.687 -3.646 -3.646 96.354 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  3.64627    1.14447   3.186  0.00165 **
## pred_change  0.02648    0.13589   0.195  0.84566   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.09 on 218 degrees of freedom
## Multiple R-squared:  0.0001742,  Adjusted R-squared:  -0.004412 
## F-statistic: 0.03798 on 1 and 218 DF,  p-value: 0.8457
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -10.323  -5.469  -2.945  -0.965  94.902 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  1.27205    1.67929   0.757   0.4496  
## trend_2012  -4.14306    2.99058  -1.385   0.1674  
## r_minus_p    0.10928    0.04535   2.409   0.0168 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14.89 on 217 degrees of freedom
## Multiple R-squared:  0.0313, Adjusted R-squared:  0.02237 
## F-statistic: 3.506 on 2 and 217 DF,  p-value: 0.03173
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012, 
##     data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -12.671  -5.193  -2.980  -0.478  92.485 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     13.03748    7.93271   1.644   0.1017  
## trend_2012      -5.36300    3.08814  -1.737   0.0839 .
## pressures_2012  -0.23553    0.09470  -2.487   0.0136 *
## resilience_2012  0.02003    0.07419   0.270   0.7875  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14.84 on 216 degrees of freedom
## Multiple R-squared:  0.04152,    Adjusted R-squared:  0.02821 
## F-statistic: 3.119 on 3 and 216 DF,  p-value: 0.02699

NP: A closer look

## 
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -88.236  -9.195   6.210  14.834  88.797 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -11.7641     3.2845  -3.582 0.000486 ***
## pred_change  -0.1747     0.2246  -0.778 0.438097    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.5 on 126 degrees of freedom
##   (92 observations deleted due to missingness)
## Multiple R-squared:  0.004779,   Adjusted R-squared:  -0.003119 
## F-statistic: 0.6051 on 1 and 126 DF,  p-value: 0.4381
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -75.785 -10.850   1.616  18.371  90.589 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -8.16424    7.84396  -1.041  0.29996   
## trend_2012  -14.20002    4.24190  -3.348  0.00108 **
## r_minus_p    -0.09716    0.17187  -0.565  0.57287   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 32.23 on 125 degrees of freedom
##   (92 observations deleted due to missingness)
## Multiple R-squared:  0.08596,    Adjusted R-squared:  0.07134 
## F-statistic: 5.878 on 2 and 125 DF,  p-value: 0.003633
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012, 
##     data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -69.379 -14.572   1.445  21.109  76.628 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     -176.6990    48.8153  -3.620 0.000428 ***
## trend_2012       -14.6324     4.0655  -3.599 0.000460 ***
## pressures_2012     2.0616     0.5858   3.519 0.000606 ***
## resilience_2012    1.4617     0.4755   3.074 0.002600 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 30.88 on 124 degrees of freedom
##   (92 observations deleted due to missingness)
## Multiple R-squared:  0.1679, Adjusted R-squared:  0.1478 
## F-statistic:  8.34 on 3 and 124 DF,  p-value: 4.277e-05

TR: A closer look

## 
## Call:
## lm(formula = obs_change ~ pred_change, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -36.713  -4.751   0.566   3.269  43.557 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -1.6756     0.9277  -1.806  0.07240 . 
## pred_change   0.3445     0.1038   3.320  0.00107 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.17 on 200 degrees of freedom
##   (18 observations deleted due to missingness)
## Multiple R-squared:  0.05223,    Adjusted R-squared:  0.04749 
## F-statistic: 11.02 on 1 and 200 DF,  p-value: 0.00107
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + r_minus_p, data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -36.634  -5.071  -0.001   3.432  41.815 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -1.08879    1.14068  -0.955  0.34098   
## trend_2012   7.98655    3.02678   2.639  0.00898 **
## r_minus_p    0.02212    0.03443   0.642  0.52146   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.3 on 199 degrees of freedom
##   (18 observations deleted due to missingness)
## Multiple R-squared:  0.03531,    Adjusted R-squared:  0.02561 
## F-statistic: 3.641 on 2 and 199 DF,  p-value: 0.02798
## 
## 
## Call:
## lm(formula = obs_change ~ trend_2012 + pressures_2012 + resilience_2012, 
##     data = data_g)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -36.673  -5.530  -0.070   3.738  41.609 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     19.23777    8.39340   2.292   0.0230 *
## trend_2012       5.41557    3.16935   1.709   0.0891 .
## pressures_2012  -0.24073    0.09570  -2.515   0.0127 *
## resilience_2012 -0.11101    0.06422  -1.729   0.0854 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.16 on 198 degrees of freedom
##   (18 observations deleted due to missingness)
## Multiple R-squared:  0.06355,    Adjusted R-squared:  0.04936 
## F-statistic: 4.479 on 3 and 198 DF,  p-value: 0.004565